Developments and current status of cell-free DNA in the early detection and management of hepatocellular carcinoma

Circulating tumor DNA in management of primary liver malignancy: A review of the literature and future directions

Primary liver malignancies are a serious and challenging global health concern. The most common primary tumors are hepatocellular carcinoma and cholangiocarcinoma. These diseases portend poor prognosis when presenting with progressive, extensive disease. There is a critical need for improved diagnosis, therapeutic intervention, and monitoring surveillance in liver-related malignancies. Liquid biopsy using ctDNA provides an opportunity for growth within these domains for liver-related malignancy. However, ctDNA is relatively understudied in this field compared with other solid tumor types, possibly due to the complex nature of the pathology. In this review, we aim to discuss ctDNA, the current literature, and future directions of this technology within primary liver malignancies.

Deep learning model based on contrast-enhanced ultrasound for predicting vessels encapsulating tumor clusters in hepatocellular carcinoma

OBJECTIVES

To establish and validate a non-invasive deep learning (DL) model based on contrast-enhanced ultrasound (CEUS) to predict vessels encapsulating tumor clusters (VETC) patterns in hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

This retrospective study included consecutive HCC patients with preoperative CEUS images and available tissue specimens. Patients were randomly allocated into the training and test cohorts. CEUS images were analyzed using the ResNet-18 convolutional neural network for the development and validation of the VETC predictive model. The predictive value for postoperative early recurrence (ER) of the proposed model was further evaluated.

RESULTS

A total of 242 patients were enrolled finally, including 195 in the training cohort (54.6 ± 11.2 years, 178 males) and 47 in the test cohort (55.1 ± 10.6 years, 40 males). The DL model (DL signature) achieved favorable performance in both the training cohort (area under the receiver operating characteristics curve [AUC]: 0.92, 95% confidence interval [CI]: 0.88-0.96) and test cohort (AUC: 0.90, 95% CI: 0.82-0.99). The stratified analysis demonstrated good discrimination of DL signature regardless of tumor size. Moreover, the DL signature was found independently correlated with postoperative ER (hazard ratio [HR]: 1.99, 95% CI: 1.29-3.06, p = 0.002). C-indexes of 0.70 and 0.73 were achieved when the DL signature was used to predict ER independently and combined with clinical features.

CONCLUSION

The proposed DL signature provides a non-invasive and practical method for VETC-HCC prediction, and contributes to the identification of patients with high risk of postoperative ER.

CLINICAL RELEVANCE STATEMENT

This DL model based on contrast-enhanced US displayed an important role in non-invasive diagnosis and prognostication for patients with VETC-HCC, which was helpful in individualized management.

KEY POINTS

Preoperative biopsy to determine VETC status in HCC patients is limited. The contrast-enhanced DL model provides a non-invasive tool for the prediction of VETC-HCC. The proposed deep-learning signature assisted in identifying patients with a high risk of postoperative ER.

Low serum double-stranded DNA levels are associated with higher survival rates in severe COPD patients

Introduction

Damage-associated molecular patterns (DAMPs) are endogenous danger signals that alert and activate the immune system upon cellular damage or death. It has previously been shown that DAMP release is increased in patients with COPD, leading to higher levels in extracellular fluids such as serum. In the current study we investigated whether the serum levels of DAMPs were associated with survival rates in COPD patients.

Methods

A panel of seven DAMPs, consisting of HMGB1, fibrinogen, α-defensin, heat shock protein 70, S100A8, galectin-9 and double-stranded DNA (dsDNA), was measured in serum of 949 severe COPD patients. Maximally selected rank statistics was used to define cut-off values and a Cox proportional hazards model was used to evaluate the effect of high or low DAMP levels on 4-year survival. For DAMPs that were found to affect survival significantly, baseline characteristics were compared between the two DAMP groups.

Results

Out of the seven DAMPs, only dsDNA was significantly associated with 4-year survival. Patients with elevated serum level of dsDNA had higher 4-year mortality rates, lower FEV1 % predicted values and higher emphysema scores.

Discussion

In conclusion, in a clinical cohort of 949 patients with moderate-to-severe COPD, elevated serum levels of dsDNA were associated with a higher risk of death. This study further illustrates the potential role of circulating DAMPs, such as dsDNA, in the progression of COPD. Together, the results of this study suggest that levels of circulating dsDNA might serve as an additional prognostic biomarker for survival in COPD patients.

Emerging and Clinically Accepted Biomarkers for Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related death and the sixth most diagnosed malignancy worldwide. Serum alpha-fetoprotein (AFP) is the traditional, ubiquitous biomarker for HCC. However, there has been an increasing call for the use of multiple biomarkers to optimize care for these patients. AFP, AFP-L3, and prothrombin induced by vitamin K absence II (DCP) have described clinical utility for HCC, but unfortunately, they also have well established and significant limitations. Circulating tumor DNA (ctDNA), genomic glycosylation, and even totally non-invasive salivary metabolomics and/or micro-RNAS demonstrate great promise for early detection and long-term surveillance, but still require large-scale prospective validation to definitively validate their clinical validity. This review aims to provide an update on clinically available and emerging biomarkers for HCC, focusing on their respective clinical strengths and weaknesses.